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  • Gan Y, Wang Q, Huang Z and Yang L. (2024). Attention-based causal representation learning for out-of-distribution recommendation. Applied Intelligence. 10.1007/s10489-024-05835-x.

    https://link.springer.com/10.1007/s10489-024-05835-x

  • Guarrasi V, Siciliano F and Silvestri F. RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender Systems. Proceedings of the 18th ACM Conference on Recommender Systems. (1265-1269).

    https://doi.org/10.1145/3640457.3687106

  • Ma A, Yu Y, Shi C, Guo Z and Chua T. (2024). Cross-view hypergraph contrastive learning for attribute-aware recommendation. Information Processing and Management: an International Journal. 61:4. Online publication date: 1-Jul-2024.

    https://doi.org/10.1016/j.ipm.2024.103701

  • Wang Q, Wu C, Lian D and Chen E. (2023). Securing recommender system via cooperative training. World Wide Web. 26:6. (3915-3943). Online publication date: 1-Nov-2023.

    https://doi.org/10.1007/s11280-023-01214-7

  • Liu H, Lin H, Zhang X, Ma F, Chen H, Wang L, Yu H and Zhang X. Boosting Meta-Learning Cold-Start Recommendation with Graph Neural Network. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. (4105-4109).

    https://doi.org/10.1145/3583780.3615283

  • Rezaimehr F and Dadkhah C. (2023). T&TRS: robust collaborative filtering recommender systems against attacks. Multimedia Tools and Applications. 10.1007/s11042-023-16641-x. 83:11. (31701-31731).

    https://link.springer.com/10.1007/s11042-023-16641-x

  • Chizari N, Tajfar K and Moreno-García M. (2023). Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems. Information. 10.3390/info14020131. 14:2. (131).

    https://www.mdpi.com/2078-2489/14/2/131

  • Wang Q, Lian D, Wu C and Chen E. Towards Robust Recommender Systems via Triple Cooperative Defense. Web Information Systems Engineering – WISE 2022. (564-578).

    https://doi.org/10.1007/978-3-031-20891-1_40

  • Ge Y, Liu S, Fu Z, Tan J, Li Z, Xu S, Li Y, Xian Y and Zhang Y. A Survey on Trustworthy Recommender Systems. ACM Transactions on Recommender Systems. 0:0.

    https://doi.org/10.1145/3652891